On PASCAL VOC 2007 test, baseline ResNet-101 improves overall mAP and most printed class APs over baseline VGG-16. The ResNet-101 baseline+++ system trained with COCO plus VOC data and using box refinement, context, and multi-scale testing reaches 85.6% mAP; the paper prints all 20 category APs.

On PASCAL VOC 2007 test, baseline ResNet-101 improves overall mAP and most printed class APs over baseline VGG-16. The ResNet-101 baseline+++ system trained with COCO plus VOC data and using box refinement, context, and multi-scale testing reaches 85.6% mAP; the paper prints all 20 category APs. · CiteArk

Measurement results

0/63 assessed

0 supported0 disputed or conflicting63 not assessed

The complete result group stays with its claim. Assessed rows come first; their judgments do not replace the whole-claim conclusion. Expand a row for conditions, sources, and evidence history.

Each row is a measurement with its reported value, latest available observation, and cumulative evidence state.
Reported	Latest observation	Evidence judgment
73.2%	—	Not assessed
76.5%	—	Not assessed
79%	—	Not assessed
70.9%	—	Not assessed
65.5%	—	Not assessed
52.1%	—	Not assessed
83.1%	—	Not assessed
84.7%	—	Not assessed
86.4%	—	Not assessed
52%	—	Not assessed
81.9%	—	Not assessed
65.7%	—	Not assessed

On PASCAL VOC 2007 test, baseline ResNet-101 improves overall mAP and most printed class APs over baseline VGG-16. The ResNet-101 baseline+++ system trained with COCO plus VOC data and using box refinement, context, and multi-scale testing reaches 85.6% mAP; the paper prints all 20 category APs.

Reported and observed measurements

Assessments (0)

Runs (0)

Measurement results